Molecular Interactions Stabilizing the Promatrix Metalloprotease-9·Serglycin Heteromer

Inhibition of bacterial and human zinc-metalloproteases by bisphosphonate- and catechol-containing compounds

Compounds containg catechol or bisphosphonate were tested as inhibitors of the zinc metalloproteases, thermolysin (TLN), pseudolysin (PLN) and aureolysin (ALN) which are bacterial virulence factors, and the human matrix metalloproteases MMP-9 and -14. Inhibition of virulence is a putative strategy in the development of antibacterial drugs, but the inhibitors should not interfere with human enzymes. Docking indicated that the inhibitors bound MMP-9 and MMP-14 with the phenyl, biphenyl, chlorophenyl, nitrophenyl or methoxyphenyl ringsystem in the S₁'-subpocket, while these ringsystems entered the S₂'- or S₁ -subpockets or a region involving amino acids in the S₁'- and S₂'-subpockets of the bacterial enzymes. An arginine conserved among the bacterial enzymes seemed to hinder entrance deeply into the S₁'-subpocket. Only the bisphosphonate containing compound RC2 bound stronger to PLN and TLN than to MMP-9 and MMP-14. Docking indicated that the reason was that the conserved arginine (R203 in TLN and R198 in PLN) interacts with phosphate groups of RC2.

A Bittersweet Computational Journey among Glycosaminoglycans

Glycosaminoglycans (GAGs) are linear polysaccharides. In proteoglycans (PGs), they are attached to a core protein. GAGs and PGs can be found as free molecules, associated with the extracellular matrix or expressed on the cell membrane. They play a role in the regulation of a wide array of physiological and pathological processes by binding to different proteins, thus modulating their structure and function, and their concentration and availability in the microenvironment. Unfortunately, the enormous structural diversity of GAGs/PGs has hampered the development of dedicated analytical technologies and experimental models. Similarly, computational approaches (in particular, molecular modeling, docking and dynamics simulations) have not been fully exploited in glycobiology, despite their potential to demystify the complexity of GAGs/PGs at a structural and functional level. Here, we review the state-of-the art of computational approaches to studying GAGs/PGs with the aim of pointing out the “bitter” and “sweet” aspects of this field of research. Furthermore, we attempt to bridge the gap between bioinformatics and glycobiology, which have so far been kept apart by conceptual and technical differences. For this purpose, we provide computational scientists and glycobiologists with the fundamentals of these two fields of research, with the aim of creating opportunities for their combined exploitation, and thereby contributing to a substantial improvement in scientific knowledge.